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Accuracy of body mass index in predictingpre-eclampsia: bivariate meta-analysisJS Cnossen,a MMG Leeflang,b EEM de Haan,a BWJ Mol,c JAM van der Post,c

KS Khan,d G ter Riete

a Department of General Practice, b Department of Clinical Epidemiology and Biostatistics and c Department of Obstetrics and Gynaecology,

Academic Medical Center, Amsterdam, the Netherlands d Department of Obstetrics and Gynaecology, Birmingham Womens Hospital,

Birmingham, UK e Horten Center, University of Zurich, Zurich, Switzerland

Correspondence:Dr JS Cnossen, Department of General Practice, Academic Medical Center, Meibergdreef 15, 1100 DD, Amsterdam,

the Netherlands. Email [email protected]

Accepted 2 July 2007. Published OnlineEarly 28 September 2007.

Objective The objective of this study was to determine the

accuracy of body mass index (BMI) (pre-pregnancy or at booking)in predicting pre-eclampsia and to explore its potential for clinical

application.

Design Systematic review and bivariate meta-analysis.

Setting Medline, Embase, Cochrane Library, MEDION, manual

searching of reference lists of review articles and eligible primary

articles, and contact with experts.

Population Pregnant women at any level of risk in any healthcare

setting.

Methods Reviewers independently selected studies and extracted

data on study characteristics, quality, and accuracy. No language

restrictions.

Main outcome measures Pooled sensitivities and specificities

(95% CI), a summary receiver operating characteristic curve, and

corresponding likelihood ratios (LRs). The potential value of BMI

was assessed by combining its predictive capacity for different

prevalences of pre-eclampsia and the therapeutic effectiveness

(relative risk 0.90) of aspirin.

Results A total of 36 studies, testing 1 699 073 pregnant women

(60 584 women with pre-eclampsia), met the selection criteria. Themedian incidence of pre-eclampsia was 3.9% (interquartile range

1.46.8). The area under the curve was 0.64 with 93% of

heterogeneity explained by threshold differences. Pooled estimates

(95% CI) for all studies with a BMI 25 were 47% (3361) for

sensitivity and 73% (6483) for specificity; and 21% (1231) and

92% (8995) for a BMI 35. Corresponding LRs (95% CI) were

1.7 (0.311.9) for BMI 25 and 0.73 (0.222.45) for BMI < 25,

and 2.7 (1.07.3) for BMI 35 and 0.86 (0.681.07) for BMI < 35.

The number needed to treat with aspirin to prevent one case of

pre-eclampsia ranges from 714 (no testing, low-risk women) to 37

(BMI 35, high-risk women).

Conclusions BMI appears to be a fairly weak predictor for pre-

eclampsia. Although BMI is virtually free of cost, noninvasive, and

ubiquitously available, its usefulness as a stand-alone test for risk

stratification must await formal cost-utility analysis. The findings

of this review may serve as input for such analyses.

Keywords Accuracy, body mass index, likelihood ratio, meta-

analysis, pre-eclampsia, sensitivity and specificity.

Please cite this paper as: Cnossen J, Leeflang M, de Haan E, Mol B, van der Post J, Khan K, ter Riet G. Accuracy of body mass index in predicting pre-eclampsia:

bivariate meta-analysis. BJOG 2007;114:14771485.

Introduction

Pre-eclampsia is a major cause of maternal and perinatalmorbidity and mortality.13 It has a long preclinical phase

before signs and symptoms become manifest in the second

half of pregnancy. Prediction of pre-eclampsia is important

because increased monitoring and administration of early

preventative treatment with, for example, aspirin can be more

selectively targeted at high-risk women.4

The exact aetiology of pre-eclampsia is still unknown. Sev-

eral risk factors have been identified including obesity.5 The

World Health Organization (WHO) estimates that currently

more than 1 billion adults are overweight, of whom at least

300 million are clinically obese.6 From 1999 to 2003, in thetotal delivery population, the proportion of women who were

overweight or obese pre-pregnancy increased from 37.1 to

40.5%.7 This worldwide increase in overweight is likely to

cause a rise in pre-eclampsia and other complications, such

as gestational diabetes.8 The body mass index (BMI) is an

international standard for obesity measurement adjusting

bodyweight for height (weight [kg]/height squared [m2])

and an indicator of nutritional status.

2007 The Authors Journal compilation RCOG 2007 BJOG An International Journal of Obstetrics and Gynaecology

1477

DOI: 10.1111/j.1471-0528.2007.01483.x

www.blackwellpublishing.com/bjogSystematic review


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We conducted a systematic review to determine the accu-

racy of BMI (pre-pregnancy or at booking) to predict pre-

eclampsia and to explore its potential for clinical application.

We meta-analysed the data using a bivariate regression anal-

ysis, accounting for (negative) correlation between sensitivity

and specificity, and derived likelihood ratios (LRs) thereof.912

Methods

Study identification and selectionWe performed an electronic search targeting all procedures

used for the prediction of pre-eclampsia. We searched Med-

line, Embase, Cochrane Library, and MEDION (www.

mediondatabase.nl/) from inception to April 2006. The search

strategy13 consisted of MeSH or keyword terms related to pre-

eclampsia combined with methodological filters for identifi-

cation of studies on diagnostic tests and aetiology.1416 We

checked reference lists of review articles and eligible primary

studies to identify cited articles not captured by electronic

searches and contacted experts. Reference Manager 10.0 data-bases were established incorporating results of all searches.

We selected studies in a three-stage process. First, one

reviewer scrutinised titles and/or abstracts of all citations.

Second, to ensure independent duplicate selection for this

review, we performed a search based on keywords *weight*

OR body mass* OR obesity OR adipos* OR fat OR quetelet*

within the Reference Manager database for citations to be

scrutinised by a second reviewer. We obtained full manu-

scripts of all citations that were selected by at least one of

the reviewers. Final in/exclusion decisions were made after

independent and duplicate examination of the full manu-

scripts of selected citations. We included studies if they

reported numerical data allowing construction of a 2 2 table

cross-classifying BMI test results (pre-pregnancy or at

booking) and occurrence of pre-eclampsia. We included

studies examining pregnant women at any level of risk in

any healthcare setting. Language restrictions were not applied.

Any disagreements were resolved by consensus or by a third

reviewer.

For each included paper, two reviewers independently

extracted data on clinical and methodological study charac-

teristics and on test accuracy. Study characteristics consisted

of womens risk classifications, characteristics of the index test

and of the reference standard, and details of the outcome,

such as onset and severity of pre-eclampsia.

Assessment of study qualityTwo reviewers independently assessed all included manu-

scripts for study quality according to an adapted version of

QUADAS.17 We considered consecutive or random entry of

pregnant women to a cohort as ensuring an appropriate spec-

trum of patients. Patient selection criteria were considered

appropriate when the study reported on parity, singleton/

multiple pregnancies, diabetes mellitus, and chronic hyper-

tension. We deemed the reference standard for pre-eclampsia

appropriate if it combined persistent systolic blood pressure

140 mmHg and/or diastolic blood pressure 90 mmHg with

proteinuria 0.3 g/24 hours or 1+ dipstick (30 mg/dl in

a single urine sample) new after 20 weeks of gestation. Sim-

ilarly, we deemed the reference standard for superimposed

pre-eclampsia appropriate when it combined the develop-

ment of proteinuria 0.3 g/24 hours or 1+ dipstick after

20 weeks of gestation in chronically hypertensive women.18,19

A definition of pre-eclampsia according to recent (from the

year 2000 onwards) international guidelines was also consid-

ered appropriate.1820 We also assessed if details of the index

test (kg/m2, pre-pregnancy or before 20 weeks) and reference

test (measurement device, position of patient, and Korotkoff

phase), and a description of reasons for withdrawals were

adequate.

Data synthesis

For each study, we constructed a 2 2 table cross-classifyingBMI test results and the occurrence of pre-eclampsia. We

used receiver operating characteristic (ROC) plots to visualise

data. We used a bivariate meta-regression model to calculate

pooled estimates of sensitivity and specificity for several BMI

cutoff values and to fit a summary ROC (sROC) curve. This

method has been extensively described elsewhere.912 Briefly,

rather than using a single outcome measure per study, such as

the diagnostic odds ratio, the bivariate model preserves the

two-dimensional nature of diagnostic data in a single model.

This model incorporates the correlation that may exist

between sensitivity and specificity within studies due to pos-

sible differences in threshold between studies. The bivariate

model uses a random effects approach for both sensitivity and

specificity, allowing for heterogeneity beyond chance due to

clinical or methodological differences between studies. In

addition, the model acknowledges the difference in precision

by which sensitivity and specificity have been measured in

each study. This means that studies with a larger number of

women with pre-eclampsia receive more weight in the calcu-

lation of the pooled estimate of sensitivity, while studies with

more women without pre-eclampsia are more influential in

the pooling of specificity. We calculated a correlation coeffi-

cient (r) to show the strength and shape of the correlation

between sensitivity and specificity, and hence the amount of

variance that can be explained due to threshold differences(r2). Despite several cutoff values for BMI, for statistical rea-

sons, each study was represented once in the sROC analysis.

This was achieved by randomly sampling one 2 2 table from

each study, ensuring that all areas of the sROC curve were

represented. For clinical usefulness, we derived LRs from the

estimated sensitivities and specificities.

We defined all subgroup analyses, except one (pre-

pregnancy or at booking), a priori requiring that at least 80%

Cnossen et al.

1478 2007 The Authors Journal compilation RCOG 2007 BJOG An International Journal of Obstetrics and Gynaecology


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of studies reported clearly on a particular item and each

subgroup contained at least three studies. Data on threshold,

pre-pregnancy or booking BMI, severity and onset (before or

after 34 weeks) of pre-eclampsia, incidence (cutoff 4%), study

design, consecutive entry, prospective data collection, and

adequacy of reference standard were considered one by one

in the subgroup analyses. First, we entered the subgroup char-

acteristic as a covariate in the model. If homogeneity between

studies with and without the characteristic was rejected

(P< 0.10), the subgroup analysis was performed.

The bivariate models were fitted using the Proc NLMixed

(SAS 9.1 for Windows [SAS Institute Inc, Cary, NC, USA]).

Potential for clinical applicationAt a (fixed) relative risk (RR) reduction of aspirin of 10%

(RR = 0.90),4 the absolute treatment effect becomes greater

as the absolute risk rises. For a woman with a risk of pre-

eclampsia of 40%, aspirin reduces it to 36%, whereas for

a woman who has a 4% risk, this is reduced to 3.6%. Testing

makes sense if treatment is conditional on obtaining a positivetest result. Therefore, the impact of risk stratification using

BMI depends on the shift that testing brings about in the pre-

test probability distribution.21 We illustrated this impact with

an example of decision-making in clinical practice about the

use of aspirin in women at risk of pre-eclampsia, which has

been shown to be an effective preventive treatment.4 We cal-

culated the risk, and hence the therapeutic benefits associated

with BMI test results from sensitivity and specificity. The

number needed to treat (NNTreat) is the number of women

that one needs to treat (with aspirin) to prevent one case of

pre-eclampsia and is calculated by 1/(probability after testing

positive probability after treatment).

Results

Figure 1 summarises the process of literature identification

and selection. Thirty-six studies met the selection criteria.2257

Four papers were found in reference lists, of which one was

included. The two papers from Sebire et al.46,47 reported on

the same cohort, and therefore, we considered it as one study.

We excluded studies for a variety of reasons. The main rea-

sons for exclusion were no distinction between pre-eclampsia

and pregnancy-induced hypertension, insufficient data to

construct a 2 2 table, and reporting weight or weight gain

or a different weight-index test instead of a BMI.

Table 1 summarises study characteristics. We included 23

cohort studies, 11 casecontrol studies, and one randomised

trial. A total of 1 699 073 pregnant women were included, of

whom 60 584 (3.6%) developed pre-eclampsia. The medianincidence of pre-eclampsia in cohort studies was 3.9% (inter-

quartile range 1.46.8). For calculation of the median inci-

dence, we excluded casecontrol studies that did not report

the incidence in their source population. The number of

women analysed in cohort studies ranged from 28127 to

561 770,56 and in casecontrol studies from 5437 to 6747.28

Twenty-seven studies were performed in Western countries.

Eleven studies were prospectively designed. Sixteen studies

excluded all women with diabetes and/or chronic hypertension.

Primary articles retrieved for detailed evaluation

- fromelectronic searches n = 133

- from reference lists n = 4

Articles excluded n = 101

- no BMI calculated n = 22

- insufficient data toconstruct 2 x 2 table n = 20

- no pre-pregnancy BMI versus pre-eclampsia outcome n = 13

- reviews/ letters/ comments/ editorials n = 5

- pre-eclampsia not separate outcome/ pregnancy induced hypertension only

n = 25

- matching on BMI n = 3

- gestational age unclear n = 1

- data duplication n = 7

- other n = 5

Primary articles included in meta-analysis n = 36

Potentially relevant citations identified from electronic searches to capture primary articles

on all tests used in the prediction of pre-eclampsia n = 17847

Potentially relevant citations on BMI in the prediction of pre-eclampsia n = 1307

References excluded after screening titles and/ or abstracts n = 1170

Figure 1. Process from initial search to final inclusion for studies on BMI in the prediction of pre-eclampsia.

Body mass index and prediction of pre-eclampsia


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Twenty-eight studies reported pre-pregnancy BMI, and seven

studies reported BMI at booking. We excluded one study for

the meta-analysis because this study excluded women with

a BMI between 27 and 34.25

Figure 2 summarises study quality. Patient recruitment,

selection criteria, and reasons for withdrawal were poorly

reported. Eleven studies used a definition of pre-eclampsia

according to current international standards. Fifteen studies

reported a definition of pre-eclampsia according to former

guidelines. Details of how the reference test was executed were

seldom reported. Only one study reported whether women

received treatment to prevent pre-eclampsia.48

Figure 3 shows the sROC curve for the included studies. The

area under the curve (AUC) was 0.64, which was similar to the

AUC representing all data points for all studies (AUC 0.63).

The correlation coefficient was 0.965, indicating that 93% of

heterogeneity could be explained due to differences in thresh-

old for a positive test. Subgroup analysis on pre-pregnancy

BMI versus BMI at booking yielded statistically significant

differences (P< 0.0001), showing slightly better performancefor pre-pregnancy BMI. However, their clinical relevance seems

doubtful (sensitivitypre-pregnancy 48.1% (47.948.2) versus

sensitivityat booking44.9% (44.445.5) and specificitypre-pregnancy61.4% (61.461.5) versus specificityat booking58.5% (58.558.6).

The discrepancy between statistical significance and clinical

relevance is due to some of the studies having very large sizes.

Below, we report the overall results. Incidence of pre-eclampsia,

study design, data collection, and adequacy of reference standard

did not further reduce heterogeneity. Analyses on clinical char-

acteristics and consecutive entry were hindered by a lack of

reporting. We used cutoff values that were most often used

and reported by the Institute of Medicine (IOM) and WHO

for categorisation.6,58 Pooled estimates (95% CI) for all studies

with a BMI 25 (18 studies) were 47% (3361) for sensitivity

and 73% (6483) for specificity. For a BMI 30 (19 studies),

these estimates were 19% (1920) and 90% (8893), and for

a BMI 35 (4 studies), the estimates were 21% (1231) and

92% (8995). Pooling data on BMI 30 required an adjusted

Table1.(Continued)

Firstauthor(year)

Co

untry

Typeofstudy

Numberof

womenanalysed

Incidence

PET(%)

Gestational

age(BMI)

Population

Sebire46,47(2001)

UK

Retrospectivecohort

325395

0.8

Atbooking

IN:unselectedpopulation

Sibai48(1997)

US

A

Prospectiverandomisedcontrolled

trial

4310

7.6

Firstvisit

(1321weeks)

IN:normotensivenulliparouswomen

;EX:medicalor

obstetriccomplications,knowfetalcomplications

Steinfeld49(2000)

US

A

Retrospectivecohort

2424

5.3

Pre-pregnancy

IN:singletonpregnancies

Stone50(1994)

US

A

Retrospectivecohort

19034

0.4

Pre-pregnancy

IN:normotensivewomen,singleton

pregnancies;

EX:lessseverecases,DM,renal/autoimmune/

cardiovasculardiseases

Suzuki51(2000)

Jap

an

Cohort

3446

8.6

Pre-pregnancy

IN:normotensivewomen;EX:mono

chorionic

twinsandsystemicillnesses

Thadhani52(1999)

US

A

Prospectivecohort

17211

0.5

Pre-pregnancy

IN:normotensivewomen

vanHoorn53(2002)

Au

stralia

Retrospectivecasecontrol

448

n.r.

Atbooking

IN:primigravid,singletonpregnancies;

EX:DM,CHandmedicationuse

Weiss54(2004)

US

A

Prospectivecohort

16102

2.4

1014weeks

IN:unselectedpopulation,singleton

pregnancies

Yamamoto55(2001)

Jap

an

Casecontrol

224

n.r.

,9weeks

IN:normotensivewomen;EX:firstvisitafter

9weeksofgestation,CH,DMandcardiovascular

disease

CH,chronichypertension;DM

,diabetesmellitus;EX,excluded;IN,included;

n.r.,notreported;PET,pre-eclampsia.

12

2

35

11

12

10

23

1

16

5

32

8

23

20

0% 20% 40% 60% 80% 100%

Withdrawals explained

Adequate description reference test

Adequate description index test

Appropriate reference test

Selection criteria

Patient spectrum

Compliance with quality item

yes no unclear

Figure 2. Summary of study quality. Data are presented as 100%

stacked bars. Numbers in stacks represent numbers of studies.



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model of the bivariate analysis resulting in a very narrow confi-

dence interval for sensitivity. Therefore, we also calculated esti-

mates for BMI 29 (seven studies, IOM classification), which

were 22% (1232) for sensitivity and 89% (8295) for speci-

ficity. The corresponding LRs (95% CI) were 1.7 (0.311.9) for

BMI 25 and 0.73 (0.222.45) for BMI < 25, 1.9 (0.311.9) for

BMI 29 and 0.88 (0.611.29) for BMI < 29, and 2.7 (1.07.3)

for BMI 35 and 0.86 (0.681.07) for BMI < 35.

Table 2 shows that the probability of pre-eclampsia after

positive BMI testing increases strongly in moderate (say inci-

dence 7%) and high-risk (say 12%) populations to 18 and

29%, respectively, for women with a BMI 35. These latter

posttest probabilities correspond with NNTsaspirinof 62 and

37, respectively. BMI results < 35 lead to posttest probabilities

of 1.2, 5.9, and 10.7% in populations with a baseline risk of

1.4, 6.8, and 12.2%, respectively. These numbers, in turn,

correspond to NNTsaspirinof 830, 170, and 94.

Discussion and conclusion

Based on a huge body of evidence, BMI (at any cutoff), pre-

pregnancy or at booking, appears to be a fairly weak predictor

for pre-eclampsia. Although BMI is virtually free of cost, non-

invasive, and ubiquitously available, its usefulness as a stand-

alone test for risk stratification must await formal cost-utility

analysis. The findings of this review may serve to populate

such a model.

Based on the estimates in this review, among low-risk preg-nancies without testing, one needed to treat 714 women with

aspirin to prevent one case of pre-eclampsia. By contrast, in

high-risk women, who in addition have a BMI 35, one

needed to treat only 37 women to prevent one case. This

may seem impressive. However, it is not entirely clear how

clinicians may decide on the exact risk level a woman has

before modifying it further using BMI. Note also that a single

test with modest values of sensitivity implies that many with

a negative test result will develop pre-eclampsia and thus, in

this example, will not benefit from what appears to be the

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

1-specificity

Sensitivity

Figure 3. sROC curve for individual studies on the accuracy of BMI inpredicting pre-eclampsia.

Table 2. Illustration of potential impact of BMI testing at two cutoff values among pregnant women at different prevalences and numbersof women needed to treat with aspirin to prevent one case of pre-eclampsia

Test result Prior risk

(prevalence of PET, %)*

Probability of PET after

testing positive (%)

Treatment effect

(RR)

Probability of PET

after treatment (%)

NNTreat

No test, no treatment** 1.4 1.4 1.4

6.8 6.8 6.8

12.2 12.2 12.2

No test, treat all** 1.4 0.90 714

6.8 147

12.2 82

BMI 35; sensitivity 21%; specificity 92%

Test all, treat test positives 1.4 3.6 0.90 3.2 278

6.8 16.1 14.5 62

12.2 26.7 24.0 37

BMI 25; sensitivity 47%; specificity 73%

Test all, treat test positives 1.4 2.4 0.90 2.2 417

6.8 11.3 10.2 88

12.2 19.5 17.6 51

PET, pre-eclampsia.

*Interquartile ranges of median prevalence in this review used as low and moderate prior risk. 12.2% was used as high prior risk.

**Numbers are equal for all tests regardless of threshold and sensitivity and specificity.

Cnossen et al.



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cheap and safe treatment of aspirin. The moderate positive

and negative LRs of a BMI > 35 underscore these conclusions.

In 2003, OBrienet al.59 reviewed 13 studies and also reported

on the predictive power of BMI for pre-eclampsia. However,

they pooled predictive values, an accuracy parameter that is

generally considered too sensitive to variations in preva-

lence.60 Duckitt and Harrington,61 in their review, covering

different tests at booking, included ten studies on BMI and

reported different summary estimates, without explaining

why they selected a RR of pre-eclampsia of 2.47 in their

abstract. In addition, their methodology is flawed by the

adoption of a cause-effect framework discarding studies with

baseline incomparability, although this is unimportant in

(noncausal) studies on prediction.62 Finally, a pooled RR

(of 2.47) does not allow clinicians to update their pre-test

pre-eclampsia probabilities for women below the cutoff value,

since such women remain on their prior probability. In con-

trast, we pooled sensitivities and specificities, which are less

susceptible to variations in prevalence60 and are more useful

in determining whether the posttest probability increases ordecreases for the various BMI categories.

The mechanism by which obesity causes pre-eclampsia is

not completely understood, although establishing a causal

link is not particularly relevant for purposes of developing

a predictive test. Obesity has a strong link with insulin resis-

tance and predisposes for type II diabetes and gestational

diabetes. Both obesity and diabetes are associated with

a higher risk of cardiovascular diseases, and hence pre-

eclampsia.5,63,64 Hyperinsulinaemia may directly predispose

to hypertension by increased renal sodium reabsorption and

stimulation of the sympathetic nervous system. Another

possible explanation is that insulin resistance and/or associ-

ated hyperglycaemia impair endothelial function.5,64 Studies

that include a high proportion of women with diabetes

or chronic hypertensive may thus find higher estimates for

test accuracy of BMI. In contrast, smoking reduces the risk

of pre-eclampsia and has no effect on the risk of gestational

diabetes.65,66 Smokers usually have a lower BMI than non-

smokers, however, this effect is reduced when they quit

smoking.67 Our estimates of predictive accuracy may be

biased due to the inability to adjust for these factors. How-

ever, this drawback holds for all meta-analyses focusing on

evaluation of a single test, whereas in clinical practice more

information is available.

Identification of primary studies on pre-eclampsia andBMI in database searches is difficult. In primary studies,

BMI is often reported as a secondary exposure in a table

showing some general population characteristics. Electronic

retrieval is possible only when BMI is mentioned in the title,

abstract, or as a keyword. Factors that may influence the pre-

dictive accuracy of BMI are poor reporting of selection crite-

ria, preventive treatment, and self-reported weight and

height. Self-reported weight in particular may be underesti-

mated in women68 and may result in a lower BMI (more test

negatives), thus lowering sensitivity and raising specificity.

However, a subgroup analysis on pre-pregnancy BMI versus

BMI at booking did not indicate this to be a strong phenom-

enon, although statistically significant. Due to lack of report-

ing on early and late onset, and on severity of pre-eclampsia,

we were unable to estimate the predictive accuracy of BMI for

severe early onset pre-eclampsia, which has considerable

maternal and perinatal morbidity and mortality.2,3

Future research should focus on undertaking high-quality

primary studies of test accuracy including all risk indicators

that clinicians normally use. This will enable future reviewers

to focus on meta-analysis of adjusted accuracy parameters or,

even better, on meta-analysis using individual patient data.69

The proper role of BMI among other tests in rational risk

stratification strategies for pre-eclampsia must await the

results from such analyses ideally incorporating the costs

and benefits of preventive treatment.

Funding

This study is supported by the UK NHS Health Technology

Assessment Programme, project code 01/64/04. j

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